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DOE OSTI2024

Anomaly detection with flow-based fast calorimeter simulators

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The caloflow model is designed to generate single-photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions, and, thus, we expect that the ultimate performance will require a combination of these approaches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

The Simons Observatory: Combining cross-spectral foreground cleaning with multitracer B -mode delensing for improved constraints on inflation

The Simons Observatory (SO), due to start full science operations in early 2025, aims to set tight constraints on inflationary physics by inferring the tensor-to-scalar ratio r from measurements of cosmic microwave background (CMB) polarization B-modes. Its nominal design including three small-aperture telescopes (SATs) targets a precision σ⁡(r = 0) ≤ 0.003 without delensing. Achieving this goal and further reducing uncertainties requires a thorough understanding and mitigation of other large-scale B-mode sources such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline aiming to estimate r by including delensing within a cross-spectral likelihood, and demonstrate it for the first time on SO-like simulations accounting for various levels of foreground complexity, inhomogeneous noise and partial sky coverage. As introduced in an earlier SO delensing paper, lensing B-modes are synthesized using internal CMB lensing reconstructions as well as Planck-like cosmic infrared background maps and LSST-like galaxy density maps. We then extend SO’s power-spectrum-based foreground-cleaning algorithm to include all auto- and cross-spectra between the lensing template and the SAT B-modes in the likelihood function. This allows us to constrain r and the parameters of our foreground model simultaneously. Within this framework, we demonstrate the equivalence of map-based and cross-spectral delensing and use it to motivate an optimized pixel-weighting scheme for power spectrum estimation. We start by validating our pipeline in the simplistic case of uniform foreground spectral energy distributions. In the absence of primordial B-modes, we find that the 1⁢σ statistical uncertainty on r, σ⁡(r), decreases by 37% as a result of delensing. Tensor modes at the level of r = 0.01 are successfully detected by our pipeline. Even when using more realistic foreground models including spatial variations in the dust and synchrotron spectral properties, we obtain unbiased estimates of r both with and without delensing by employing the moment-expansion method. In this case, uncertainties are increased due to the higher number of model parameters, and delensing-related improvements range between 27% and 31%. These results constitute the first realistic assessment of the delensing performance at SO’s nominal sensitivity level.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at z = 0.25 to 10% at z = 0.95 , to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multiobservable likelihood for the Sunyaev-Zel’dovich effect, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol ECS, and SPTpol 500d surveys and the DES Year 3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Unifying simulation and inference with normalizing flows

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. Published by the American Physical Society 2025

Hadronic calorimiters↗

DOE OSTI2021

Detecting hidden layers from spreading dynamics on complex networks

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. Furthermore, we conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Classical Simulation of Boson Sampling Based on Graph Structure

Boson sampling is a fundamentally and practically important task that can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In this Letter, we present classical sampling algorithms for single-photon and Gaussian input states that take advantage of a graph structure of a linear-optical circuit. The algorithms’ complexity grows as so-called treewidth, which is closely related to the connectivity of a given linear-optical circuit. Using the algorithms, we study approximated simulations for local Haar-random linear-optical circuits. For equally spaced initial sources, we show that, when the circuit depth is less than the quadratic in the lattice spacing, the efficient simulation is possible with an exponentially small error. Notably, right after this depth, photons start to interfere each other and the algorithms’ complexity becomes subexponential in the number of sources, implying that there is a sharp transition of its complexity. Finally, when a circuit is sufficiently deep enough for photons to typically propagate to all modes, the complexity becomes exponential as generic sampling algorithms. We numerically implement a likelihood test with a recent Gaussian boson sampling experiment and show that the treewidth-based algorithm with a limited treewidth renders a larger likelihood than the experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗

DOE OSTI2022

Measurement of the $c$-jet mistagging efficiency in $t\bar{t}$ events using $pp$ collision data at $\sqrt{s}=13$ $\text {TeV}$ collected with the ATLAS detector

A technique is presented to measure the efficiency with which c-jets are mistagged as b-jets (mistagging efficiency) using tt¯ events, where one of the W bosons decays into an electron or muon and a neutrino and the other decays into a quark–antiquark pair. The measurement utilises the relatively large and known W → cs branching ratio, which allows a measurement to be made in an inclusive c-jet sample. The data sample used was collected by the ATLAS detector at √s = 13 TeV and corresponds to an integrated luminosity of 139 fb –1 . Events are reconstructed using a kinematic likelihood technique which selects the mapping between jets and tt¯ decay products that yields the highest likelihood value. The distribution of the b-tagging discriminant for jets from the hadronic W decays in data is compared with that in simulation to extract the mistagging efficiency as a function of jet transverse momentum. The total uncertainties are in the range 3–17%. The measurements generally agree with those in simulation but there are some differences in the region corresponding to the most stringent b-jet tagging requirement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Observational constraints on early dark energy

In this paper, we review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam and KiDS+VIKING-450, as well as BOSS/eBOSS galaxy clustering and Lyman-[Formula: see text] forest data. We detail the fit to CMB data, and perform the first analyses of EDE using the CAMSPEC and Hillipop likelihoods for Planck CMB data, rather than Plik, both of which yield a tighter upper bound on the allowed EDE fraction than that found with Plik. We then supplement CMB data with LSS data in a series of new analyses. All these analyses are concordant in their Bayesian preference for [Formula: see text]CDM over EDE, as indicated by marginalized posterior distributions. We perform a series of tests of the impact of priors in these results, and compare with frequentist analyses based on the profile likelihood, finding qualitative agreement with the Bayesian results. All these tests suggest prior volume effects are not a determining factor in analyses of EDE. This work provides both a review of existing constraints and several new analyses.

Astronomy & Astrophysics↗

DOE OSTI2025

Seismic Contingency Auto Generator

This code takes in premade earthquake scenario XML files from USGS, power grid data, and converts them into a contingency file (.con file) that can be used by power grid solvers. Within the .con file are a number (Specified by the user) of contingencies that have randomly failed power transformers based on their likelihood of failure and peak ground acceleration (PGA) value around the transformer. The transformers' likelihood of failure was calculated based on a variety of finite element modeling on various transformer designed for specific transformer voltage classes. Parameters from these FEM were used to create generic fragility curves for transformers within a specific voltage class, which correspond with earthquake PGA values to produced a probability of failure for a given earthquake scenario. More refined versions of this process, such as specifying specific transformer design categories within a voltage class, could also be applied in future iterations of the software.

Vaagensmith, Bjorn [Idaho National Laboratory (INL↗

DOE OSTI2021

The Role of Radiative Interactions in Tropical Cyclone Development under Realistic Boundary Conditions

Abstract The impact of radiative interactions on tropical cyclone (TC) climatology is investigated using a global, TC-permitting general circulation model (GCM) with realistic boundary conditions. In this model, synoptic-scale radiative interactions are suppressed by overwriting the model-generated atmospheric radiative cooling rates with their monthly varying climatological values. When radiative interactions are suppressed, the global TC frequency is significantly reduced, indicating that radiative interactions are a critical component of TC development even in the presence of spatially varying boundary conditions. The reduced TC activity is primarily due to a decrease in the frequency of pre-TC synoptic disturbances (“seeds”), whereas the likelihood that the seeds undergo cyclogenesis is less affected. When radiative interactions are suppressed, TC genesis shifts toward coastal regions, whereas TC lysis locations stay almost unchanged; together the distance between genesis and lysis is shortened, reducing TC duration. In a warmer climate, the magnitude of TC reduction from suppressing radiative interactions is diminished due to the larger contribution from latent heat release with increased sea surface temperatures. These results highlight the importance of radiative interactions in modulating the frequency and duration of TCs.

Meteorology & Atmospheric Sciences↗

DOE OSTI2022

Quantifying Flood Frequency Associated with Clustered Mesoscale Convective Systems in the United States

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. In this work, our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Abstract With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet to be established and different studies on uncertainties generally uses different metrics to evaluate them. We compare three of the most popular validation metrics (Spearman’s rank correlation coefficient, the negative log likelihood (NLL) and the miscalibration area) to the error-based calibration introduced by Levi et al. ( Sensors 2022 , 22 , 5540). Importantly, metrics such as the negative log likelihood (NLL) and Spearman’s rank correlation coefficient bear little information in themselves. We therefore introduce reference values obtained through errors simulated directly from the uncertainty distribution. The different metrics target different properties and we show how to interpret them, but we generally find the best overall validation to be done based on the error-based calibration plot introduced by Levi et al. Finally, we illustrate the sensitivity of ranking-based methods (e.g. Spearman’s rank correlation coefficient) towards test set design by using the same toy model ferent test sets and obtaining vastly different metrics (0.05 vs. 0.65).

Rasmussen, Maria H.↗

DOE OSTI2020

Farmers’ Perceptions of Marginal Land for Biofuel Crops

The dataset includes the survey results about farmers’ perceptions of marginal land availability and the likelihood of a land pixel being marginal based on a machine learning model trained from the survey. Two spreadsheet files are the farmer and farm characteristics (marginal_land_survey_data_shared.xlsx), and the existing land use of marginal lands (land_use_info_sharing.xlsx). Note: the blank cells in these two spreadsheets mean missing values in the survey response. The GeoTiff file includes two bands, one the marginal land likelihood in the Midwestern states (0-1), the other the dominant reason of land marginality (0-5; 0 for farm size, 1 for growing season precipitation, 2 for root zone soil water capacity, 3 for average slope, 4 for growing season mean temperature, and 5 for growing season diurnal range of temperature). To read the data, please use a GIS software such as ArcGIS or QGIS.

marginal land↗

DOE OSTI2021

Phylogenetic diversity of 200+ isolates of the ectomycorrhizal fungus Cenococcum geophilum associated with Populus trichocarpa soils in the Pacific Northwest, USA and comparison to globally distributed representatives

The ectomycorrhizal fungal symbiont Cenococcum geophilum is of high interest as it is globally distributed, associates with many plant species, and has resistance to multiple environmental stressors. C . geophilum is only known from asexual states but is often considered a cryptic species complex, since extreme phylogenetic divergence is often observed within nearly morphologically identical strains. Alternatively, C . geophilum may represent a highly diverse single species, which would suggest cryptic but frequent recombination. Here we describe a new isolate collection of 229 C . geophilum isolates from soils under Populus trichocarpa at 123 collection sites spanning a ~283 mile north-south transect in Western Washington and Oregon, USA (PNW). To further understanding of the phylogenetic relationships within C . geophilum , we performed maximum likelihood and Bayesian phylogenetic analyses to assess divergence within the PNW isolate collection, as well as a global phylogenetic analysis of 789 isolates with publicly available data from the United States, Japan, and European countries. Phylogenetic analyses of the PNW isolates revealed three distinct phylogenetic groups, with 15 clades that strongly resolved at >80% bootstrap support based on a GAPDH phylogeny and one clade segregating strongly in two principle component analyses. The abundance and representation of PNW isolate clades varied greatly across the North-South range, including a monophyletic group of isolates that spanned nearly the entire gradient at ~250 miles. A direct comparison between the GAPDH and ITS rRNA gene region phylogenies, combined with additional analyses revealed stark incongruence between the ITS and GAPDH gene regions, consistent with intra-species recombination between PNW isolates. In the global isolate collection phylogeny, 34 clades were strongly resolved using Maximum Likelihood and Bayesian approaches (at >80% MLBS and >0.90 BPP respectively), with some clades having intra- and intercontinental distributions. Together these data are highly suggestive of divergence within multiple cryptic species, however additional analyses such as higher resolution genotype-by-sequencing approaches are needed to distinguish potential species boundaries and the mode and tempo of recombination patterns.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

PacWave South SeaRAY k2 Risk Registers

The SeaRAY is a deployable power system for maritime sensors, monitoring equipment, communications, unmanned underwater vehicles, and other similar payloads. This project is to design, deliver, and test a prototype low-power WEC that lowers the total cost of ownership and provides robust, new capabilities for customers in the maritime environment. Failure Modes, Effects, and Criticality Analysis (FMECA) is conducted to systematically identify all potential failure modes and their effects on the system, and to analyze the criticality of each risk based on the likelihood of the event and the severity of the impact. Actions may then be recommended to mitigate the criticality of a risk, either by reducing the likelihood of the risk or the severity of its impact. Risk assessment is executed iteratively as an integral part of the design process. By incorporating risk assessment early in the development cycle, mitigation of risk can be achieved cost effectively. The actions recommended to mitigate risk may be subsequently executed, and as the design progresses the risk assessment is reviewed and revised. Review of the risk assessment is integrated into structured design reviews, ensuring that critical risks are comprehended and that the Project will not progress to e.g. fabrication while intolerable risks remain. The risk assessment process results in the population and maintenance of Risk Registers (RRs). Each major system (and as needed, subsystem) will have a distinct RR. This allows each system or subsystem to be assessed individually, rendering the RRs to a manageable size for review.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Characterizing the Sample Selection for Supernova Cosmology

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.

79 ASTRONOMY AND ASTROPHYSICS↗